The human cerebral ventricular system contains four intercommunicating chambers, the left lateral ventricle, the right lateral ventricle, the third ventricle, and the fourth ventricle. The ventricles contain cerebrospinal fluid (CSF) and changes in CSF volume and shape are associated with several neurological diseases such as congenital anomalies, post-traumatic disorders, pseudo-tumors, neuro-degenerative diseases, inflammatory diseases, headache, cognitive dysfunction/psychiatric diseases and post-operative changes. Quantification of the degree of dilatation of ventricles is important in the diagnosis of various diseases and for measuring the response to treatment. It may also be important in predicting the prognosis of the disease process. Therefore, extraction and quantification of the ventricular system from medical images is of primary importance.
Normally all of the ventricles are interconnected to enable circulation of cerebrospinal fluid. Hence, in an ideal case, a single region growing operation should be able to extract a complete ventricular system. In reality, however, this does not work and many factors should be taken into account such as noise, spatial and contrast resolutions of the scan. In particular, the method has to handle a partial volume effect causing “leakages” from the ventricles to the extraventricular space, and intraventricular and extraventricular pathology distorting the normal anatomy of the ventricular system.
Conventionally, the standard methods employed for investigating the human cerebral ventricular system have been ventriculography and pneumoencephalography, each of which has relatively high morbidity and mortality. With the advent of high speed Computed Tomography (CT) and three-dimensional (3D) magnetic resonance (MR) imaging (MRI), robust techniques are required to extract and quantify the ventricular system.
Due to the great importance of the ventricular system, its extraction has been the subject of extensive research work resulting in methods such as region-growing assisted by morphological operations, thresholding, template matching, atlas warping, level sets, active models, and knowledge based methods.
Schnack H G, Hulshoff P H E, Baare W F C, Viergever M A, Kahn R S, “Automatic segmentation of the ventricular system from MR images of the human brain,” NeuroImage 2001 vol. 14, pp. 95-104 describes an automatic algorithm to extract the lateral and third ventricles from T1-weighted 3-D-FFE MR images of the human brain. The algorithm is based upon region-growing and mathematical morphology operators. One of the limitations of this algorithm is its dependence on the coarse binary brain segmentation image and the coarse white matter segmentation image. Although precise white matter segmentation is not necessary, precise brain segmentation is essential to ventricular system extraction. Another limitation is that it is not able to avoid inclusion of non-ventricular CSP regions although some anatomical knowledge of the ventricular system has been incorporated into the method to guide the location of the seed point(s) and searching subregions. Furthermore, the algorithm described is not able to extract the complete ventricular system as the fourth ventricle is not extracted. Several artificial boundaries are required when extracting the third ventricle resulting in some amount of manual editing needed to obtain precise results. In addition, the algorithm is time consuming to run (about 5 to 20 min on a Pentium3 450 MHz PC for a brain volume).
Worth A J, Makris N, Patti M R, Goodman J M, Hoge E A, Caviness V S, Kennedy D N, “Precise segmentation of the lateral ventricles and caudate nucleus in MR brain images using anatomically driven histograms,” IEEE Transactions on Medical Imaging 1998, vol. 17, no. 2, pp. 303-310 describes an automated method to extract the lateral ventricles and caudate nucleus on T1-weighted coronal MR brain images of normal subjects. The algorithm derives, automatically, intensity thresholds from anatomical information and a histogram, and then extracts the outlines of the lateral ventricle and caudate nucleus by generating isointensity contours. The algorithm uses knowledge guided rules and methods for locating automatically certain anatomical landmarks (for example, the corpus callosum), and employs the local thresholds for extraction of the lateral ventricle and caudate nucleus. The algorithm is able to extract only the lateral ventricles in normal subjects. It needs manual editing of the resulting outlines due to irregularities in automatically generated outlines caused by the partial volume effect and low contrast. It takes about 50 minutes to extract the lateral ventricles and caudate for a brain volume data.
Kaus M R, Warfield S K, Nabavi A, Black P M, Jolesz F A, Kikinis R, “Automated segmentation of MR images of brain tumors,” Radiology 2001, vol. 218, no. 2, pp. 586-591 discloses an adaptive template-moderated classification method to extract ventricles and brain tumors. The algorithm iterates statistical classification to assign labels to tissue types and non-linear registration to align a digital anatomic atlas to the patient data. Statistical classification is used to divide an image into different tissue classes on the basis of signal intensity values. If different tissues have the same or overlapping gray-value distributions, additional information about the spatial location of anatomic structures is derived from a registered anatomic atlas. As such, the algorithm is dependent on the digital atlas and is time consuming to run (5-10 minutes).
Kildnis R, Shenton M E, Losifescu D V, McCarley R W, Saiviroonporn P, Hokama H H, Robation A, Metcalf D, Wible C G, Portas C M, Donnino R M, Jolesz F A, “A digital brain atlas for surgical planning model-driven segmentation and teaching,” IEEE Transactions on Visualization and Computer Graphics 1996, vol. 2, no. 3, pp. 232-241 describes an automated model-based segmentation system that uses a digital brain atlas to extract brain structure. When MR images do not provide sufficient contrast between various structures, a combination of automated and supervised segmentation methods along with the atlas information is used to define regions of interest (for example, the ventricular system) and to guide a segmentation algorithm based on neuroanatomical knowledge. The anatomical atlas is registered with classified 3D images and an elastic matching method is used for the projection of atlas information onto the data sets of subjects and patients. The resulting segmentation of the ventricular system depends on the accuracy of the digital brain atlas, and the accuracy of the matching method. The algorithm works best with images of normal subjects and is time consuming to run.
Baillard C, Hellier P, Barillot C, “Segmentation of 3D brain structures using level sets and dense registration,” IEEE Workshop on Mathematical Methods on Biomedical Image Analysis (MMBIA 2000), pp. 94-101 describes a co-operative strategy for the segmentation of parts of ventricles from 3D brain MRI images which integrates 3D segmentation and 3D registration methods. The segmentation is based on the level set formalism. Using an automatic registration method to initialise the ventricular structure surface, the algorithm iteratively propagates the closed 3D surface towards the desired boundaries through the evolution of a 4D implicit function. The major limitations of this method are that it can only extract parts of ventricles and that it is time consuming.
Cootes T, Taylor C, Cooper D H, Graham J, “Active shape models—their training and application,” Computer Vision and Image Understanding 1995, vol. 61, no. 1, pp. 38-59 discloses a statistical technique for building deformable shape templates and uses these models to extract various organs, including the ventricles, from 2D and 3D medical images. The statistical formulation provides global shape constraints and allows the model to deform only in ways implied by the training set. The shape of the models represent objects by sets of landmark points that are placed in the same way on an object boundary in each input image. The points can be connected to form a deformable contour. By examining the statistics of training sets of manually-labelled medical images, and using principal component analysis, a shape model is derived that describes the average positions and the major models of variation of the object points. New shapes are generated using the mean shape and a weighted sum of the major modes of variation. Object boundaries are then extracted using this “point distribution model” by examining a region around each model point to calculate the displacement required to move it towards the boundary. These displacements are then used to update the shape parameter weights. There are several problems with this approach. The technique is sensitive to the initial position of the atlas, that is, if the initial rigid alignment is off by too much, the elastic match may perform poorly. The presence of neighbouring features may also cause matching problems, for example, the atlas may warp to an incorrect boundary. Without user interaction, the atlas can have difficulty converging to complicated object boundaries.
Wang Y, Staib L H, “Boundary finding with correspondence using statistical shape models,” Proceeding IEEE conference of computer vision and pattern recognition 1998, pp. 338-345 proposes an approach for finding the boundary where the correspondence of a subset of boundary points using local shape features of a model is simultaneously determined. Statistical point models are constructed with shape and shape variation generated from sets of examples using principal component analysis of the covariance matrix. The model is then analysed in a Bayesian scheme to find shape parameters and pose parameters. The algorithm is used to locate the boundary of the lateral ventricles. Although the statistical point model is able to capture considerable variability for the lateral ventricular boundary, it is not easy to build the statistical model specific to all classes of the ventricular system. Furthermore, this approach is time consuming.
In 1998, as described in http://www-sig.enst.fr/tsi/groups/TII/active, a way of computing the correspondence between an MR volume and the atlas was proposed in which structural information (as flexible spatial constraints) was taken into account. The spatial constraints were formulated by the adoption of fuzzy set theory and information fusion theory. Segmentation approaches were not used globally but conditionally to regions of interest with imprecise limits. The calculation for correspondence between the MRI data and the atlas inferred a discrete deformation field constrained by object surfaces. Those recognised objects were relied upon to extract the whole ventricular system. This appears to be the first method (until 1998) to include the fourth ventricle. The aforementioned method needs to be validated on a large set of images and there does not appear to be any published information concerning its ability to distinguish the ventricles which would enable the method to be assessed. From the results presented, the algorithm does not appear to be able to extract the ventricular system accurately, or to maintain the connectivity automatically.
Sonka M, Tadikonda S K, Collins S M, “Knowledge-based interpretation of MR brain images,” IEEE Transactions on Medical Imaging 1996, vol. 15, no. 4, pp. 443-452 describes a fully automated segmentation method to extract brain structures including the ventricular system from MR images. The algorithm is based on a hypothesize-and-verify principle and uses a genetic algorithm (GA) optimisation technique to generate and evaluate image interpretation hypotheses in a feedback loop. The algorithm is trained on 20 out of 28 MR brain images, with observer-defined contours of structures being used as prior knowledge and incorporated in the genetic algorithm (GA)-based image interpretation method. The method is tested on the remaining eight brain images, and can produce accurate labelling results of neuroanatomical structures. One limitation is that it can only handle brain with no gross anatomic abnormality. In addition, the manual identification of contours of neuroanatomical structures is tedious, time-consuming and vulnerable to inter-personal variations.
Holden M, Schnable J A, Hill D L G, “Quantifying small changes in brain ventricular volume using non-rigid registration,” MICCAI 2001, pp. 49˜56 describes the implementation of a non-rigid registration algorithm based on optimising normalised mutual information to extract the lateral ventricles from MR images. The algorithm uses a free-form deformation (FFD) to model local deformation, with the FFD being constructed from a 3D tensor product of B-splines, and deformation being achieved by translating control points in steps along the direction with maximum gradient until either the magnitude of the gradient is less than or equal to a threshold, or a pre-specified number of iterations is reached. By propagation, the algorithm maps the patient MR images with a generic atlas without the necessity for subject-specific segmentation. In this way, they first have a strong confidence in their model and then progressively take into account the additional information coming from the data itself. If the local refinements to be performed are small, the algorithm behaves well. However, if the patient's brain has been deformed too much, for example, due to high variability or disease, the model does not work well. The algorithm works best with images of normal subjects.
As described in http://www.mevis.de/projects/volumetry/volumetry.html, Center for Medical Diagnostic Systems and Visualisation, University of Bremen, Hahn et al developed a semiautomatic extracting and volumetric analysis algorithm of the cerebral ventricles from MR image. A few marks were manually defined as initial information for four cerebral ventricles and then the ventricular borders in 3D space were traced automatically by an algorithm that combined some concepts of classical transformation with a region fusion. The processing time was less than 1 second for a typical region of interest and the complete volumetric procedure can be performed in less than 5 minutes. Due to the complexity of the ventricle system and partial volume effect of MR images, the algorithm required a fine spatial resolution for the MR image data (for instance 0.5 mm). In addition to the human interaction for initial information, the algorithm is not able to maintain the connectivity automatically.
Lundervold A, Storvik G, “Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images,” IEEE Transactions on Medical Imaging 1995, vol. 14, no. 2, pp. 339-349 describes a model-based segmentation method to extract, automatically, brain parenchyma and CSF in axial multispectral MR images. The algorithm incorporates information about anatomical boundaries and tissue signature using prior knowledge. One limitation of the algorithm is that it is restricted to slice images where the brain parenchyma and CSF spaces form connected regions.
DeCarli C, Horwitz B, “Method for quantification of brain volume from magnetic resonance images,” USA patent US005262945A, 1993 describes a semi-automated method for regional analysis of brain, central and subarachnoid CSF volumes from MR images. The method is based on mathematical modelling of MR pixel intensity histograms. The histogram is modelled as a Gaussian allowing the application of standard statistical moments to pixel distribution.
All pixels for T2-weighted MR images are considered to be CSF when the intensity of the pixels is greater than (mean value−n*standard deviation) (where n is a constant to be set differently for different images as well as different brain structures). This is misleading because, in reality, one value of ‘n’ or an accurate threshold for separating the CSF from the brain matter signals is not enough owing to the partial volume effect and intensity inhomogeneity. In addition, it is difficult to locate, automatically, the threshold pixel intensity from the intersection of Gaussian fitting curves because the number of modals of the histogram of a ROI is difficult to determine.
Fisher E, Rudick R A, “Method and system for brain volume analysis,” USA patent US006366797B1, 2002 describes a method for brain volume analysis from MRI images. Firstly, the brain is separated from other connected structures, and the brain surface contours are identified automatically. Then, the total volume within the brain surface contours is calculated. Fluid filled regions are thereby excluded and this accounts for the partial volume effect. The brain volume is then normalized by the total contour volume, and the brain parenchymal fraction is generated. The brain parenchymal fraction serves as a reliable measurement of brain atrophy and assists in determining the severity and progression of multiple sclerosis or other conditions. This quantification work develops a reliable method for automatic generation of a starting point for segmentation of cerebral structures, such as the lateral ventricles, by using active surfaces or deformable models, as described in Sturm B, Meier D, Fisher E, “Automated approximation of lateral ventricular shape in magnetic resonance images of multiple sclerosis patients,” MICCAI 2002, pp. 483-491. However, this method it is too crude for ventricular volume analysis.
Brandt M E, “Method and apparatus for estimating tissue volumes in magnetic resonance images”, USA patent US005425368A, 1995 describes a fuzzy approach to distinguish CSF, grey matter (GM), and white matter (WM) pixels in MR brain images. An unsupervised fuzzy clustering procedure based on a variation of the fuzzy c-means algorithm computes automatically the percentage area of each of these three compartments in each image. Although the approach does not assume any a priori statistical or heuristic model of the data, the method requires the input of the number of different compartments in the images and a parameter which determines the amount of overlap of compartment boundaries. Additionally, two or more different spectral channels of the same MR images are needed so as to get more accurate discrimination of tissue types. The approach requires about two minutes to analyse a single brain MR image to yield a decision on the percent of GM, WM, and CSF.
Gosche K M, “Method and system for knowledge guided hyperintensity detection and volumetric measurement,” USA patent US006430430B1, 2002 describes an automated method and system for identifying suspected lesions in a multi-spectral dataset of the brain. The system applies a validity-guided clustering segmentation technique suitable for the discovery of small clusters to classify the varying pixel intensities into separate groupings, which potentially correspond to different tissue types. Then, the system refines the initial segmentation results into the separate groupings using one or more knowledge rules that combine pixel intensities with the spatial relationship of anatomical structures to locate one or more anatomical regions of the brain. Although the system uses one or more knowledge rules that combine pixel intensities with spatial relationship of anatomical structures to locate one or more anatomical regions of the brain, the system is suitable to detect brain lesion but not the ventricular system using multi-spectral MRI dataset.
Despite the numerous approaches proposed to solve the problem of extracting the human cerebral ventricular system, the above-described methods suffer from disadvantages and, as such, are not suitable for clinical use. This is due to the necessity for human intervention, the inability of these methods to handle pathological and abnormal cases, and/or the inability to extract the complete ventricular system. Moreover, the existing methods are too slow to be accepted clinically. Due to the anatomical complexity of the cerebral ventricular system and the lack of fast and reliable segmentation procedures, a fast, automatic, accurate, and robust method is desirable to extract the complete ventricular system.
The present invention is directed to ameliorating or overcoming the above problems of prior art methods.